Deep learning based automatic detection algorithm for acute intracranial haemorrhage: a pivotal randomized clinical trial

Acute intracranial haemorrhage (AIH) is a potentially life-threatening emergency that requires prompt and accurate assessment and management. This study aims to develop and validate an artificial intelligence (AI) algorithm for diagnosing AIH using brain-computed tomography (CT) images. A retrospective, multi-reader, pivotal, crossover, randomised study was performed to validate the performance of an AI algorithm was trained using 104,666 slices from 3010 patients. Brain CT images (12,663 slices from 296 patients) were evaluated by nine reviewers belonging to one of the three subgroups (non-radiologist physicians, n = 3; board-certified radiologists, n = 3; and neuroradiologists, n = 3) with and without the aid of our AI algorithm. Sensitivity, specificity, and accuracy were compared between AI-unassisted and AI-assisted interpretations using the chi-square test. Brain CT interpretation with AI assistance results in significantly higher diagnostic accuracy than that without AI assistance (0.9703 vs. 0.9471, p < 0.0001, patient-wise). Among the three subgroups of reviewers, non-radiologist physicians demonstrate the greatest improvement in diagnostic accuracy for brain CT interpretation with AI assistance compared to that without AI assistance. For board-certified radiologists, the diagnostic accuracy for brain CT interpretation is significantly higher with AI assistance than without AI assistance. For neuroradiologists, although brain CT interpretation with AI assistance results in a trend for higher diagnostic accuracy compared to that without AI assistance, the difference does not reach statistical significance. For the detection of AIH, brain CT interpretation with AI assistance results in better diagnostic performance than that without AI assistance, with the most significant improvement observed for non-radiologist physicians.


Source of Monetary / Material Support
Organisation Name SK Organisation Type Others Project ID SKAIICH-01

Sponsor Organisation
Organisation Name Ajou University Hospital Organisation Type Medical Institute

Lay Summary 1. Purpose of Clinical Trial
This clinical trial was designed to evaluate the efficacy of 'SKH-BCH-001' a software that assists medical staff in determining the priority of brain haemorrhage diagnosis by analysing brain computed tomography (CT) images without contrast agent in slice units, and then using an artificial intelligence algorithm based on a convolutional neural network (CNN) to segment brain regions and model abnormal regions, and by detecting the location with a high probability of brain haemorrhage. The primary purpose of this clinical trial is to prove that the clinical sensitivity and specificity of the medical equipment(software) 2. Background Intracranial haemorrhage can be diagnosed by using computed tomography (CT) and magnetic resonance imaging (MRI). CT scan is useful because it has the advantage of being able to proceed with the test relatively quickly and quickly discriminating whether or not there is a intracranial haemorrhage. However, certain factors of CT, such as signal to noise, signal attenuation, and artifacts, may have a negative effect on diagnosing a lesion and thus may lead to misdiagnosis. According to previous studies, discrepancies between the initial and final diagnosis results may lead to misdiagnosis. The discrepancy result for intracranial haemorrhage accounted for 13.6% of these results, and among them, the misdiagnosed type of intracranial haemorrhage consisted of 39.0% of traumatic subdural haemorrhage (SDH) and 33.0% of subarachnoid haemorrhage (SAH), respectively. Therefore, it is necessary to reduce the misdiagnosis of intracranial haemorrhage and increase the diagnostic efficacy.
Accordingly, SK Inc. has developed 'SKH-BCH-001', an automated prioritisation software that assists medical staff in determining the priority of brain haemorrhage diagnosis by analysing brain computed tomography (CT) images without contrast agent in slice units, and then using an artificial intelligence algorithm based on a convolutional neural network (CNN) to segment brain regions and model abnormal regions, and by detecting the location with a high probability of brain haemorrhage. It is expected that the efficacy of diagnosis will increase as it can make a quick diagnosis. 3.Medical devices for clinical trials -Testing equipment for clinical trials Medical Insight + Brain Haemorrhage (SKH-BCH-001) Purpose of Use: The product is a software that assists medical staff in determining the priority of brain haemorrhage diagnosis by analysing brain computed tomography (CT) images without contrast agent in slice units, and then using an artificial intelligence algorithm based on a convolutional neural network (CNN) to segment brain regions and model abnormal regions, and by detecting the location with a high probability of brain haemorrhage. 4. Clinical trial period: it is expected that it will take a total of 3 months, including 1 month for medical data collection and screening and 2 months for image reading. It is also expected that it will take an additional 4 months to prepare the report for results. 5. Target images: Brain CT image taken for diagnosis of intracranial haemorrhage (ICH). 6. Number of target images: A total of 296 images (148 images of intracranial haemorrhage and 148 images of normal patients). 7. Clinical trial design: Multicentre, randomized, retrospective, confirmatory 8. Clinical trial method Observing the accuracy when using AI and not using AI for 9 image readers to verify the accuracy improvement effect when using AI 9. Efficacy Assessment Variables -AUC of ROC Curve (Area Under the Curve of Receiver Operating Characteristic Curve) for the algorithm of the test device.
-Sensitivity for algorithm of the test device (Clinical Sensitivity, %) -Specificity for algorithm of the test device (Clinical Specificity, %)

Study Type
Interventional